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Unit of study_

COMP5349: Cloud Computing

This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'. Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud. In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The unit will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.

Code COMP5349
Academic unit Computer Science
Credit points 6
COMP4349 OR OCMP5349
Assumed knowledge:
Basic programming skills as covered in INFO1110 or INFO1910 or ENGG1810 or COMP9001 or COMP9003. Knowledge of OS concepts as covered in INFO1112 or COMP9201 or COMP9601 would be an advantage.

At the completion of this unit, you should be able to:

  • LO1. describe and analyze the execution plan of various big data workloads
  • LO2. describe the fundamental techniques in cloud computing such as data center infrastructures, virtualization and container technology, partitioning, replication and fault tolerance
  • LO3. describe and compare key principles and implementation details of cloud services like infrastructure, platform, storage and software services
  • LO4. describe resource scheduling at various levels, e.g. VM, container and programming
  • LO5. explain various algorithms for distributed data consistency such as 2PC and Paxos
  • LO6. design and implement big data analytic workload using various frameworks
  • LO7. apply functional programming paradigm to design big data analytic workload
  • LO8. analyze the execution performance of big data analytic workload based on hardware configuration and parameter setting
  • LO9. evaluate the performance of various algorithms on a specific analytic workload.

Unit outlines

Unit outlines will be available 1 week before the first day of teaching for the relevant session.